{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "04746978",
   "metadata": {},
   "source": [
    "## BA无标度网络的度动力学"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "364db549",
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1e0a4889",
   "metadata": {},
   "outputs": [],
   "source": [
    "def barabasi_albert_graph(n0, n, m, i0, i1, i2):\n",
    "    # 假定初始网络是一个包含n0个节点的完全网络\n",
    "    G = nx.complete_graph(n0)\n",
    "    targets = list(range(m))\n",
    "    repeated_nodes = list(range(n0))*m\n",
    "    source = n0\n",
    "    deg_i0 = []\n",
    "    deg_i1 = []\n",
    "    deg_i2 = []\n",
    "    while source < n:\n",
    "        degree_list = [G.degree(i) for i in G.nodes()]\n",
    "        if i0 < len(G.nodes()):\n",
    "            deg_i0.append(degree_list[i0])\n",
    "        if i1 < len(G.nodes()):\n",
    "            deg_i1.append(degree_list[i1])\n",
    "        if i2 < len(G.nodes()):\n",
    "            deg_i2.append(degree_list[i2])\n",
    "        G.add_edges_from(zip([source] * m, targets))\n",
    "        repeated_nodes.extend(targets)\n",
    "        repeated_nodes.extend([source] * m)\n",
    "        targets = random.sample(repeated_nodes, m)\n",
    "        targets = set(targets)\n",
    "        while len(targets) < m:\n",
    "            x = random.choice(repeated_nodes)\n",
    "            targets.add(x)\n",
    "        source += 1\n",
    "    return G, deg_i0, deg_i1, deg_i2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "550f4030",
   "metadata": {},
   "source": [
    "#### 单个样本"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d71e1155",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n0 = 3\n",
    "m = 2\n",
    "n = 10000\n",
    "\n",
    "# 设置指定节点的适应度值\n",
    "i0, i1, i2 = 1, 10, 100\n",
    "\n",
    "G, deg_i0, deg_i1, deg_i2 = barabasi_albert_graph(n0, n, m, i0, i1, i2)\n",
    "\n",
    "x = list(range(1,n+1))\n",
    "xx0 = np.arange(x[0], x[len(x) - 1], 0.01)\n",
    "yy0 = (10 ** 1) * xx0 ** (0.5)\n",
    "\n",
    "# 绘制指定节点的度与时间的依赖关系\n",
    "plt.plot(range(i0, i0+len(deg_i0)), deg_i0, 'r-', label='node '+str(i0))\n",
    "plt.plot(range(i1, i1+len(deg_i1)), deg_i1, 'b-', label='node '+str(i1))\n",
    "plt.plot(range(i2, i2+len(deg_i2)), deg_i2, 'g-', label='node '+str(i2))\n",
    "plt.plot(xx0, yy0, \"k--\", linewidth=1.0, label=r'$k(t) \\sim t^{\\beta}, \\beta = 0.5$')\n",
    "\n",
    "plt.legend(loc=0)\n",
    "plt.xscale(\"log\")\n",
    "plt.yscale(\"log\")\n",
    "plt.xlabel('$t$')\n",
    "plt.ylabel('$k(t)$')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43e2be06",
   "metadata": {},
   "source": [
    "#### 多个样本的统计平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "762cf564",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n0 = 3\n",
    "m = 2\n",
    "n = 10000\n",
    "\n",
    "# 设置指定节点的适应度值\n",
    "i0, i1, i2 = 1, 10, 100\n",
    "\n",
    "samples = 10\n",
    "y0 = np.zeros(9997)\n",
    "y1 = np.zeros(9989)\n",
    "y2 = np.zeros(9899)\n",
    "for i in range(samples):\n",
    "    G, deg_i0, deg_i1, deg_i2 = barabasi_albert_graph(n0, n, m, i0, i1, i2)\n",
    "    y0 += np.array(deg_i0)\n",
    "    y1 += np.array(deg_i1)\n",
    "    y2 += np.array(deg_i2)\n",
    "\n",
    "# print(len(deg_i0),len(deg_i1),len(deg_i2))\n",
    "\n",
    "x = list(range(1,n+1))\n",
    "xx0 = np.arange(x[0], x[len(x) - 1], 0.01)\n",
    "yy0 = (10 ** 1) * xx0 ** (0.5)\n",
    "print(\"==============\")\n",
    "\n",
    "\n",
    "# 绘制指定节点的度与时间的依赖关系\n",
    "plt.plot(range(i0, i0+len(y0)), y0/samples, 'r-', label='node '+str(i0))\n",
    "plt.plot(range(i1, i1+len(y1)), y1/samples, 'b-', label='node '+str(i1))\n",
    "plt.plot(range(i2, i2+len(y2)), y2/samples, 'g-', label='node '+str(i2))\n",
    "plt.plot(xx0, yy0, \"k--\", linewidth=1.0, label=r'$k(t) \\sim t^{\\beta}, \\beta = 0.5$')\n",
    "\n",
    "plt.legend(loc=0)\n",
    "plt.xscale(\"log\")\n",
    "plt.yscale(\"log\")\n",
    "plt.xlabel('$t$')\n",
    "plt.ylabel('$k(t)$')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0d4230c",
   "metadata": {},
   "source": [
    "## 绘制BA无标度网络的度分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f3265e7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import *\n",
    "import powerlaw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cb6c7785",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$p(k)$')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n0 = 3\n",
    "\n",
    "N = 100000\n",
    "m = [3,5,7]\n",
    "\n",
    "G1 = barabasi_albert_graph(n0, N, m[0])\n",
    "degree_seq1 = [G1.degree(i) for i in G1.nodes()]\n",
    "\n",
    "G2 = barabasi_albert_graph(n0, N, m[1])\n",
    "degree_seq2 = [G2.degree(i) for i in G2.nodes()]\n",
    "\n",
    "G3 = barabasi_albert_graph(n0, N, m[2])\n",
    "degree_seq3 = [G3.degree(i) for i in G3.nodes()]\n",
    "\n",
    "\n",
    "# 对数坐标，对数分箱\n",
    "powerlaw.plot_pdf(degree_seq1, linear_bins = False, color = 'r', marker='o', linewidth=0.0, label='$m = $'+str(m[0]))\n",
    "powerlaw.plot_pdf(degree_seq2, linear_bins = False, color = 'b', marker='s', linewidth=0.0, label='$m = $'+str(m[1]))\n",
    "powerlaw.plot_pdf(degree_seq3, linear_bins = False, color = 'g', marker='*', linewidth=0.0, label='$m = $'+str(m[2]))\n",
    "plt.legend(loc=0)\n",
    "\n",
    "plt.xlabel(\"$k$\")\n",
    "plt.ylabel(\"$p(k)$\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "23df1891",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$p(k)$')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = [50000,100000,200000]\n",
    "m = 3\n",
    "\n",
    "G1 = barabasi_albert_graph(n0, N[0], m)\n",
    "degree_seq1 = [G1.degree(i) for i in G1.nodes()]\n",
    "\n",
    "G2 = barabasi_albert_graph(n0, N[1], m)\n",
    "degree_seq2 = [G2.degree(i) for i in G2.nodes()]\n",
    "\n",
    "G3 = barabasi_albert_graph(n0, N[2], m)\n",
    "degree_seq3 = [G3.degree(i) for i in G3.nodes()]\n",
    "\n",
    "# 对数坐标，对数分箱\n",
    "powerlaw.plot_pdf(degree_seq1, linear_bins = False, color = 'r', marker='o', linewidth=0.0, label='$N = $'+str(N[0]))\n",
    "powerlaw.plot_pdf(degree_seq2, linear_bins = False, color = 'b', marker='s', linewidth=0.0, label='$N = $'+str(N[1]))\n",
    "powerlaw.plot_pdf(degree_seq3, linear_bins = False, color = 'g', marker='*', linewidth=0.0, label='$N = $'+str(N[2]))\n",
    "plt.legend(loc=0)\n",
    "\n",
    "plt.xlabel(\"$k$\")\n",
    "plt.ylabel(\"$p(k)$\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ee20ad5",
   "metadata": {},
   "source": [
    "## 对数分箱绘制度分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "96e46501",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2ea523f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'P(k)')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n0 = 3\n",
    "n = 100000\n",
    "m = 3\n",
    "\n",
    "G = barabasi_albert_graph(n0, n, m)\n",
    "degree = dict(nx.degree(G)).values()\n",
    "bins = np.round(np.logspace(np.log10(min(degree)), np.log10(max(degree)), 20)) # 对数间隔\n",
    "cnt = Counter(np.digitize(np.array(list(degree)), bins))\n",
    "\n",
    "x, y = list(zip(*[(bins[k-1], v/n) for k, v in cnt.items()]))\n",
    "plt.plot(x, y, 'o')\n",
    "plt.xscale(\"log\")\n",
    "plt.yscale(\"log\")\n",
    "plt.xlabel(\"Degree k\")\n",
    "plt.ylabel(\"P(k)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd6e3661",
   "metadata": {},
   "source": [
    "## 绘制累积度分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0a757a42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$P_c(k)$')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k, Pk = get_pdf(G)\n",
    "Pck = np.array([sum(Pk[i:]) for i in range(len(Pk))])\n",
    "\n",
    "plt.plot(k, Pck, 'o')\n",
    "plt.xscale(\"log\")\n",
    "plt.yscale(\"log\")\n",
    "plt.xlabel(\"Degree k\")\n",
    "plt.ylabel(\"$P_c(k)$\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "843ddba7",
   "metadata": {},
   "source": [
    "## BA无标度网络的直径和集聚系数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ef9cf96d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.rc('font', family='SimHei')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2e125520",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '$<d>$')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n0 = 3\n",
    "m = 3\n",
    "\n",
    "N = [10,20,30,50,70,100,200,300,400,500,700,1000,2000]\n",
    "\n",
    "# 计算BA无标度网络的平均距离、平均集聚系数与尺寸N的依赖关系\n",
    "C = [] # 平均集聚系数\n",
    "L = [] # 平均距离\n",
    "samples = 1\n",
    "for n in N:\n",
    "    s1 = 0\n",
    "    s2 = 0\n",
    "    for i in range(samples):\n",
    "        G = barabasi_albert_graph(n0, n, m)\n",
    "        s1 += nx.average_clustering(G)\n",
    "        s2 += nx.average_shortest_path_length(G)\n",
    "\n",
    "    C.append(s1 / samples)\n",
    "    L.append(s2 / samples)\n",
    "\n",
    "    \n",
    "plt.figure(figsize=(10,4))\n",
    "plt.subplot(121)\n",
    "plt.plot(N, C, 'ro')\n",
    "plt.xlabel(\"$N$\")\n",
    "plt.ylabel(\"$<C>$\")\n",
    "plt.xscale(\"log\")\n",
    "# plt.yscale(\"log\")\n",
    "plt.title(\"$<C>$\")\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(N, L, 'bs')\n",
    "plt.xlabel(\"$N$\")\n",
    "plt.ylabel(\"$<d>$\")\n",
    "plt.xscale(\"log\")\n",
    "plt.title(\"$<d>$\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9d0aa247",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rc('font', family='Times New Roman')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cab7874e",
   "metadata": {},
   "source": [
    "## 随机生长模型（偏好连接的缺失）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0fd7a8cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n0 = 3\n",
    "N = 50000\n",
    "m = 3\n",
    "\n",
    "G1 = model_A(n0, N, m)\n",
    "\n",
    "k, pk = get_pdf(G1)\n",
    "plt.plot(k, pk, \"ro\")\n",
    "plt.yscale(\"log\")\n",
    "plt.xlabel(\"$k$\")\n",
    "plt.ylabel(\"$p(k)$\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6ee028b",
   "metadata": {},
   "source": [
    "## 非线性生长模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f7e1a28d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import igraph as ig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4b6675c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义求度分布的函数(注意是针对igraph类型的图)\n",
    "def get_pdf_igraph(G):\n",
    "    all_k = G.degree()\n",
    "    k = list(set(all_k))  # 获取所有可能的度值\n",
    "    N = len(all_k)\n",
    "\n",
    "    Pk = []\n",
    "    for ki in sorted(k):\n",
    "        c = 0\n",
    "        for i in G.vs:\n",
    "            if G.degree(i) == ki:\n",
    "                c += 1\n",
    "        Pk.append(c / N)\n",
    "\n",
    "    return sorted(k), Pk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "de99b2ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NTXjttdfwl7/8JSjXuPXWW3Hrrbf6fFx8fDwyMjKCEoO3+NhLQdrbgbw8aSV4d6+8PGk/IiKKDIIgoLq0etzEBwCssKK6tBqCu8kxAXrvvfdw880347bbbsOJEyeQn5+PLVu24Kc//Sk2b97stG9NTQ2SkpI8vtqD/CF06tQpaLVaXHPNNfj2t78d9POPh5UfBTEagcFBz/sMDkr7sfpDRBQ5ls5cCr1Wj6aeJlhsV5rpioKIoswiLJ25NGTXfuyxx3D33Xfj8ccfBwCsXLkSK1euxPLly1FYWOi076OPPop77rnH4/m0Wm3QYispKcGuXbuQl5eHnp4ebN68GQsXLsTHH3+M5OTkoF3HFZMfIiKiELNXf8r/o9xpu8VmCWnVp7OzE0ePHsX27dsd2+Lj42Gz2cZUfQAgLS0NaWlpIYllPKMfk914440oKSlBTk4Ofv/732P16tUhuy4feymJt0trBHMJDiIiCgt79UcUpMkuoiBCr9WHtOpz8uRJAEBRUZFjW0tLC4qLizFnzpwx+8vx2Gu0KVOm4LrrrsNnn30WsmsArPw4hGRtL199+CGABd7tp/diPyIiUgzX6k+oqz4AYDKZIIqi4xp9fX3Yvn075s6dO+7+4X7s5cpsNuP06dP4p3/6p5BdA2Dy41BZWYnKykr09/cjNTVVniA8TfPyZz8iIlIUe/XnWPexkFd9AGDevHmwWCx49tlncffdd2P9+vXIzc1Fc3MzPv/8c+Tk5DjtH8hjL7PZ7FSxaW1txfHjx5GWlgbdyEDVF154AX/84x9x6NAhAMDGjRtx++23IycnB93d3di0aRNEUcTKlSv9/Im9w8deSqLRBHc/IiJSFEEQULO4BvmafNQsrglp1QcArr32WmzZsgU7d+5EYWEhtFotDhw4gBkzZqC8vHziE/jggw8+QGFhoWMQ9WOPPYbCwkI8+eSTjn2MRiNOnz7t+L6zsxMrV65EXl4e7rnnHkydOhXvvfce0tPTgxqbK8Fms9lCeoUIY6/8mEwmpKSkhPXaTccsmF88cePDxgYLivRskEhEFA6Dg4NobW3F1VdfDbVaLXc4Mc3TvfDl85uVHwXRTBehTvA85kidYIFmOhMfIiIif3HMj4LodEDLZyKMv38TeO454OyoVujTM4CNG6G55xb2+CEiIgoAkx+F0ekA3cZbgB/czLXAiIiIQoDJj1J5uRYYLBYmSURERD5g8hPJ9u8H1q8HOjuvbMvKAnbulFaSJyIiojE44DlS7d8PrFjhnPgAQFeXtH3/fnniIiKKUpwcLb9g3QNWfiKRxYL2yp/CaJs39j0bAAjQfP9Z6JYv5yMwIqIAJSQkAAAuXLiASZMmyRxNbBseHgYAiAF+tjH5iUDt+xqQd6Yeg3D/S6juuYiWfQ3Q3XtTGCMjIoo+oihiypQpOHv2LABg8uTJIW9OSGNZrVYYDAZMnjwZ8fGBpS9MfiKQ8bTJY+IDAIOYBONpEzgrnogocBkZGQDgSIBIHnFxcdDpdAEnn1Gd/LzzzjvYtGkTDh48KHcowcVlMIiIwkoQBGRmZmLatGm4dOmS3OHELJVKhbi4wIcrR3Xy87WvfQ0XLlyQO4zgG1k3JWj7ERGRV0RRDHi8Cckv6md7qVQquUMIPm9/8fgLSkRENEZUVH42btwIo9HotO2JJ55AQUGBTBERERGRUkVF8rN9+3a5Q1AudoAmIiJyosjHXgcPHkRJSQna2toc2wYGBrB27VpUVVVh3bp1GBoaki/ASPHmm0BuLlBaCtx3n/Q1N5cNEImIKKYpLvkxGAwwm81oaGhw2r5mzRosWbIE27Ztw4IFC1BVVTXhuT7++GOcPn0an3zyidt9hoaG0N/f7/RSOo0GUKs976NOsEDz+APsAE1ERORCsCmwX7fVaoUoimhtbUVubi66u7sxc+ZMnDt3Dmq1GgaDATk5Oejt7UVycnJA13rqqaewefPmMdtNJhNSUlICOncotbcDLsOcrrBYoLnjq9CdaRj/fUGQ1gBrbeUjMCIiigr9/f1ITU316vNbcZUfAGPm8NfX10Oj0UA9Uu5IT09HYmLimOqQP6qqqmAymRyvjo6OgM8ZDjodUFTk5jVwxH3iAwA2G9DRIY0FIiIiijERMeC5q6sLaWlpTtuSkpLQ3d0d8LkTExORmJgY8HkUpacnuPsRERFFkYhIfgRBcFR97IaHhx2LzQVDbW0tamtrYbFYgnZO2WRmoh3ZMMJ9h2cNjNBlZoYxKCIiImWIiORHq9XCZDI5bTObzdBqtUG7RmVlJSorKx3PDCNZe85C5OFTDML9qGg1BtGSk8C1v4iIKOYocsyPq9LSUnR2djqWsrc/7iouLpYzLMUynhM9Jj4AMAg1jOc42JmIiGKPIpMf+wQ0+9fMzEyUl5fj8OHDAIADBw5g7dq1Yx6FEREREU1EccmP2WxGXV0dAGD37t2OZSvq6uqwd+9ebN26FSdOnMDTTz8d1OvW1taioKAAer0+qOdVNIsFqK8Hfvtb6Ws0jHciIiKagCL7/MjJlz4BStXUBMyfP/F+jdNuRdHZN65syMoCdu4EKipCFxwREVEIRHyfHwqTs73O37P7MxERxQAmP3SFvQi4YQMfgRERUdRi8jMiJsf8jIfdn4mIKMox+RlRWVmJ5uZmHDt2TO5QAubVwqe4CA3cLQ4Gdn8mIqKoFRFNDsk3Oh3Q0uJm4dMPPgC+94jU4Rke1jFj92ciIopSTH4UpsPUAcMFg9v3p101DVkpWROeR6eTXmPMLUT7pgQYz6S7Wf5CgCYzAbqFC70PmoiIKIIw+RmhhLW9hi4PQf+iHr0DvW73yUjKQNv6NiTG+7cYa3uXiLwv3sUg3Hd3VhstaNnXAJ21TaoALVwIiOwGTURE0YFjfkYoYcyPSlRBl6pDnJvbEoc4ZKdkQyWq/L6G0QgMXvKcyAxeEmH8x0rgvvuA0lIgN5fT34mIKGow+VEQQRBQXVoNK6zjvm+FFdWl1RAEIbyBsf8PERFFESY/CrN05lLotXqIgnN1RhRE6LV6LJ25NPxBsf8PERFFESY/CmOv/lhszkmGxWaRp+pjx/4/REQUJZj8KJBr9UfWqo8r9v8hIqIIx+RnhJI6PLtWf2Sv+ozG/j9ERBThmPyMUMJsr9Hs1R8Ayqj6CAKQnS1NeyciIopgTH4UShAE1CyuQb4mHzWLa4JW9fFr6Qv7tXfsYL8fIiKKeILNZp/KQwDQ39+P1NRUmEwmpKSkyB1OSLS3j7/0RU8PcP6/GzDllReQ+cXHV96YngFs3AjNPbeM3zWaiIhIZr58fjP5cRELyc942tuBvDxgcND9PmqVBS3bX4NuTiq7PhMRkaL48vnNx14EYKTzs4fEBwAGh0UY121m12ciIopoTH5GKGm2V0Rg12ciIopQTH5GKG22l+Kx6zMREUUoJj/kP3Z9JiKiCMTkhwLHrs9ERBRBmPxQ4Nj1mYiIIki83AFQBBMEICuLXZ+JiCiiMPkhAFc6P3ua7q7CIHqQgSYUShtsgGbJcuiOHGHfHyIiihhscugiVpscAmM7P/f0ABUVwPCw+2PUuIgW5EGXZQN27pQOICIiCjM2OSS/6HRAUdGVV2am58QHAAYxCUZo2PeHiIgiBpOfEWxyGCD2/SEiogjB5GcEmxwGAfv+EBFRBGDyQ8HHvj9ERKRgTH4o+Nj3h4iIFIzJDwVXWpo05ofjfoiISKGY/FBw9fUBZWVAbi5nfhERkSIx+SG37I0PPRnd+LAJhWhHtvQGp74TEZFCscmhi1hucjie0Y0PfWp6iI4ry1+0trL7MxERhRSbHFLQjG586FPTQ4BT34mISJGY/FDoceo7EREpCJMfCj1OfSciIgVh8jOCy1uEgCAA2dnSiu9EREQKweRnBJe3CAGbDbjrLmnMD/v+EBGRQjD5odCwz+7asQMoLWXfHyIiUgwmPxR0JzEbTZYb2feHiIgUiX1+XLDPj3vt7UBeHjA46P0x7PtDREThwD4/FBI6HdDSAjQ2Sq9XXpn4GPb9ISIipYmXOwCKLDqd9AoI+/4QEZGMWPmh8GPfHyIikhGTHwqvtDRp2junvhMRkUyY/FB49fUBZWWc+k5ERLJh8kPy4NR3IiKSCZMfCrmTmI0mFDr3/bF3WNiwgY/AiIgorDjbi/ym0QBq9cR9f1Zhj+PPTn1/Rk99X7QotMESERGNiNrKj8FgwB133IGcnBw888wzcocTlQLu+2PHqe9ERBRGUVv5aWpqwr59+9Df34/Zs2fjkUceQVpamtxhRZ2g9P3h1HciIgqjqE1+li1bBgDQaDQoKCjgUhVKlZ4OfPWrckdBREQxJCqSn40bN8JoNDpte+KJJ1BQUICOjg4sW7YM8fFR8aNGhZOY7fizxmCEbuZMYOdOoKJCxqiIiChWRPXCpjabDc8//zzWr1/vdfLDhU3919QEzJ/v2zHSAOjZ0AkdwL59TICIiMgvEb+w6cGDB1FSUoK2tjbHtoGBAaxduxZVVVVYt24dhoaGJjzPnj17sHr1asTHx6O3tzeEEZO/pAHQU6VvOO2diIjCQHHJj8FggNlsRkNDg9P2NWvWYMmSJdi2bRsWLFiAqqoqj+eprq7GU089hbKyMsyePRstLS2hDJsCxRXfiYgoTBT52MtqtUIURbS2tiI3Nxfd3d2YOXMmzp07B7VaDYPBgJycHPT29iI5OTmgaw0NDTlVkfr7+5Gdnc3HXn5obwfy8ibu++OqEUUowofSN3v2ACtXBj84IiKKahH/2Csuzjms+vp6aDQaqNVqAEB6ejoSExPHVIf8sW3bNqSmpjpe2dnZAZ8zVvnT92cMTnsnIqIQU2Ty46qrq2tMj56kpCR0d3cHfO6qqiqYTCbHq6OjI+BzxjKdDigqkl75+T4ezGnvREQUBhGR/AiC4Kj62A0PDyMhISHgcycmJiIlJcXpRTIxGICZM7nYKRERhVRENL/RarUwmUxO28xmM7RabdCuUVtbi9raWlgiZLZRh6kDhgsGt+9Pu2oaslKywhiR/+x9fzQwQtfVKa32zmnvREQUIooc8AxI1R77gOeenh7MmjULfX19UKlU6O7uxqxZs/DFF1+MqQgFKhL6/AxdHkLOjhz0Drifvp+RlIG29W1IjE8MY2TOfB0A7Vj0VOgEsrKA1lZAFEMbJBERRYWIH/Bsz8fsXzMzM1FeXo7Dhw8DAA4cOIC1a9cGPfGJFCpRBV2qDnFubl8c4pCdkg2VqApzZM5GD4D2adFTTnsnIqIQUlzyYzabUVdXBwDYvXu3Y9mKuro67N27F1u3bsWJEyfw9NNPB/W6tbW1KCgogF6vD+p5Q0EQBFSXVsMK67jvW2FFdWk1BEEIc2Rj2QdA+zz4GeBq70REFBKKfewll0h47AVIVbGSl0rQ1NMEi+3KOCVREFGUWYT3H3pfEcmPnbdLXzj1/Dl4EFi8OLSBERFRVIj4x140MXv1Z3TiAwAWm0UxVR9/nMRsNKEQ7cgGvvtdzvwiIqKgY/ITwZbOXAq9Vg9RkAYFi4IIvVaPpTOXyhyZ/1ZhD+ajCXloQXtnnDTziwkQEREFEZOfEZE05sfOtfoT6VWf0bjgKRERhQqTnxGVlZVobm7GsWPH5A7FJ/bqD4CIr/qMizO/iIgoyJj8RDhBEFCzuAb5mnzULK5RbNVHowEC6kzAmV9ERBQkEdHhmTwru6YMzZXNcofhkb3nj9EInDwJrFo18TFOnZ+54CkREQUJkx8KG51OenlrFfYAGOn8fPJN6BaFJi4iIootfOw1IhIHPMeKQUyCce2TnPVFRERBwSaHLiKlyWEk87bh4WiNmI+ibAPX+yIionGxySEpmn+Dnznri4iIgoPJD4WdrwueOuGsLyIiChAHPJMsfB38fBKzpVlf06aFLigiIooJrPyM4IBnZVuFPdKSF6v+hQOfiYgoIEx+RkRqh+dYMohJMJ65zPW+iIgoIAElP5cuXUJHRwdaWlrQ19cXrJgohvg++HlkciLX+yIiIj/5nPx8+eWX+Pd//3fcfPPNSElJQW5uLvLz85Geno6cnBw8/PDDrJ6Q1+yDn30a+Mz1voiIKAA+JT/PP/88cnNz8atf/QplZWX405/+hOPHj+PTTz/F0aNHsWnTJly+fBlLly5FeXk5Tp06Faq4KYrodEB+vh8HcuYXERH5wafZXseOHcPbb7+N66+/ftz3i4uL8eCDD6Kurg6/+tWvcOTIEcyaNSsogRIBXO+LiIgC53eH57Nnz2JaFE47Zodnefja9VmNQbT8PQG6q9ntmYiIwtThecWKFbC4GXB6+fJlf08rG051jyyDUMN4jokPERH5zu/kZ8qUKVi3bt2Y7V988QXKysoCCkoOnOouL3+WvDh5EmhvD008REQUvfxOfn7961/jv//7v/Hyyy87tp08eRLFxcW46qqrghIcxQ5/lrxYtQrIy2MCREREvvF7eYspU6bg1VdfxaJFi3DDDTfg3LlzuPfee7F69Wo899xzwYyRYoSvS14AwOAgYDT6fhwREcUun5KfiooKzJs3z/GaM2cOXnjhBXzjG9/A4OAgfv7zn+OBBx4IVaxE47NYAHD8DxERecen5GfmzJk4cuQIXnjhBRiNRnzlK1/B3LlzYbPZcN9996GoqAiXLl1CQkJCqOIlGuvDDwH9ArmjICKiCOFT8jP6cVZXVxeOHz+O48ePY+rUqXjrrbfwy1/+EvHx8Zg9ezY++uijoAdLNJ6THw1D085HX0RE5B2/x/zMmDEDM2bMwG233ebYZjabcfz4cSY+FBD7zK/BQe/2X/V/vgrVS9Jap3PmMAkiIiLP/G5yGG1qa2tRW1sLi8WCTz/9lE0OZdbeDhw5bMGq+30by6NWS7PGmAAREcWWkDU5bPdxTnFXV5dP+8uJfX6URacD8q/3fRCzffYXERGROz4lP3q9Ht/73vc8JggmkwkvvvgibrjhBrz66qsBB0ixy5/Gh0RERBPxacxPc3Mznn76aSxZsgRqtRrz58+HVquFWq3GuXPn0NzcjE8++QRFRUV49tln8Y1vfCNUcVMMsDc+PPLTd7Hq/3xV7nCIiChK+FT5mTp1Kp5//nn09PTghRdewKxZs2A0GnHq1CkAwLe//W00Njbi6NGjTHwoKHQ6IH+uSu4wiIgoivg122vSpElYsWIFVqxY4RjXM2PGjKAGRuRQWOjb/mx6SEREHvi9ttc777yDq6++GjqdDjqdDtOnT8ePfvQj9Pf3BzM+IkD0MZH58MPQxEFERFHB7z4/3/ve95Cfn49XX30VKSkpePfdd7Fz5068+uqrOHz4MCtBMuswdcBwweD2/WlXTUNWSlYYI/Kfr31/Tn40DDRdOZbT3omIaDS/+/xMmjQJH330Ea677jrHNpvNhnvuuQcA8Ic//CE4EYaZL30ClGro8hByduSgd6DX7T4ZSRloW9+GxPjEMEbmv/Z24K97TqCiKg/D8D5m9v0hIooNIevzM1p+fj7Onj3rtE0QBGzZsgVvvPGGv6elIFCJKuhSdYhzc3vjEIfslGyoxMgZSKzTAbc9fj1OZXwdr+E2qDDk1XHs+0NERK78Tn6++93v4p//+Z/R0dHhtD2SKybRQhAEVJdWwwrruO9bYUV1aTUEQQhzZAESRehqf4RMnPGp+kNERDSa32N+NmzYAACYNWsWKioqMG/ePFgsFrzyyit49tlngxUf+WnpzKXQa/Vo6mmCxWZxbBcFEUWZRVg6c6mM0QWgogJ4Jhn4sdyBEBFRpPI7+enp6XEsYnr8+HHs2rULp06dgiAIePbZZ/H666/jxhtvxI033ojy8vJgxhwSo9f2igb26k/5fzj/3Vtslsis+oz2la/IHQEREUWwoC5sOjg4iL/+9a9OSdHHH3+M8+fPB+sSIRcNA57tbDYbSl4qcVR/7FWf9x96P6KTn6aaNzD/J94n1I2NQFFRCAMiIiLZ+fL57XflZzxqtRp6vR56vT6YpyU/uVZ/oqLqA0jz131w8iSnvBMR0RV+D3imyGAf+wMAeq0+csf6jOZjx+dVq4C8PGm6PBEREZOfKCcIAmoW1yBfk4+axTWRX/UBfO/4DE55JyKiK4L62IuUqeyaMjRXNssdRtD42vGZiIhoNFZ+KOLodFLX5sYGCxqn3YpXcJ/cIRERUQRh5Ycikk4H6P5+BDj7BgDvxgBx4DMREQGs/FAk6+nxafdVq4BZs4D//E8OfiYiimVMfihyZWb6fMjwMPDNb0pJ0NGjIYiJiIgUj8kPRa6FC4GsLAC+z2AbHgYWLWIFiIgoFjH5ocglisDOndDACDUu+nz48DCnvxMRxSImPxTZKiqg+/12tMQVcNYXERF5JWqTn4GBATz00EO44YYbsHfvXrnDoVBKT4fO2oZ8/E3uSIiIKAJEbfLT29uLuro6HDhwgMlPtPNx1hcREcW2qO3zc8011wAAOjs7sW7dOpmjoZDyY9YXERHFrqhIfjZu3Aijy8jVJ554AklJSaiqqkJaWhoWLVokT3AUeiOzvjSdX0CNixjEJLkjIiIiBRNsNptN7iBCyWazYd68eTh48CDS09Mn3L+/vx+pqakwmUxISUkJQ4QUFPv3AytWoN2Whb/iBnwLf8JlqCY8rLpaWiTeXjxiB2giosjky+e3Isf8HDx4ECUlJWhra3NsGxgYwNq1a1FVVYV169ZhaGjIq3MJgoDi4mKkpaWFKFpShIoKYO9e6MRu3IbX8Ta+DhUm/jfyr/8qNT2cP1965eWx9w8RUbRTXPJjMBhgNpvR0NDgtH3NmjVYsmQJtm3bhgULFqCqqsrjeXbt2oUHHngAu3fvxiOPPAJRFMfdb2hoCP39/U4vilDp6YDFAgC4Ce/jFGb5PP19cJC9f4iIop0iH3tZrVaIoojW1lbk5uaiu7sbM2fOxLlz56BWq2EwGJCTk4Pe3l4kJycHdK2nnnoKmzdvHrOdj70i0G9/C9znnOw0oRDz0eTTaRobgaKiYAZGREShFvGPveLinMOqr6+HRqOBWq0GAKSnpyMxMXFMdcgfVVVVMJlMjldHR0fA5ySZcNYXERF5ISJme3V1dY0Zs5OUlITu7u6Az52YmIjExMSAz0MKYF/rq7MzoNOwbRARUXSLiORHEARH1cdueHgYCQkJQbtGbW0tamtrYRkZMxLNOkwdMFwwuH1/2lXTkJWSFcaIgmRkrS/cdVdAp6moAE6d4qwvIqJoFRHJj1arhclkctpmNpuh1WqDdo3KykpUVlY6nhlGq6HLQ9C/qEfvQK/bfTKSMtC2vg2J8RFYEauoADZsAHbs8PsU9gVPmfwQEUUnRY75cVVaWorOzk4MDw8DgONxV3FxsZxhRSSVqIIuVYc4N7c+DnHITsmGSpy4R45iLV8udwRERKRgikx+7BPQ7F8zMzNRXl6Ow4cPAwAOHDiAtWvXjnkURhMTBAHVpdWwwjru+1ZYUV1aDUEQwhxZEC1cCMyYAQDQwAgVBmUOiIiIlERxyY/ZbEZdXR0AYPfu3Y5lK+rq6rB3715s3boVJ06cwNNPPx3U69bW1qKgoAB6vT6o51WipTOXQq/VQxScex+Jggi9Vo+lM5fKFFmQiCLwyCMAAB06UI9FXjU8JCKi2KDIPj9yipXlLf7rs/9C+X+Uj9n+xrffwLJrl8kQUZC59PxpRzaM0OAkZmMV9kx4OHv9EBFFFl8+vyNiwDMFn73609TTBIvNAlEQUZRZFPlVHzuXnj86dEAH9nAiIiIFPvai8LCP/bHYpKn9Fpsl8sf6jGbv+RMtPw8REQUNk58RsTTmx85e/QEQHWN9RrP3/AGcEiANjFDjosdDVSqp0WFTk/OLC54SEUUHjvlxEStjfuwO/v0g1r2+Dj+79Wcou6ZM7nCCb/9+YP16p67P7ZklMD5WA9xyi2NbT4/UImikm8K41GqgpYX9f4iIlMiXz28mPy5iLfmJCRYLcOSIlOFkZkqPxEQR7e1XVnA/eRJYtWriU3EgNBGRMnHAM9FooggsWuS0qb0dyMsDBtkCiIgo5jD5GRFLa3vFLIsFqK8H6uth7MnE4OBauSMiIiIZ8LGXCz72ilL790uND7/4AgDQhELMR5PPp+FjLyIiZeJjL6LR9u8PeKV3IiKKHpzqTtHNYgHWrQva6Xp6gnYqIiKSCZMfim5HjgBdXUE7XUUF+/0QEUU6Jj8U3YJcqhkevjI9noiIIhOTnxGx2OE5Jris8UVERMTZXi442yvKWCxATs6YR1/tyMYsfIphqH0+5Y4dUp9EANBo2PGZiEgJfPn8ZuWHopsoAj/72ZjNOnRgPyr8OuWGDcD8+dIrL49jgIiIIg2TH4p+FRXAq68CU6c6bc7EmYBPPTjIMUBERJGGfX7IocPUAcMFg9v3p101DVkpWWGMKIgqKoBvfhOYMYPZChFRjGPyQwCAoctD0L+oR+9Ar9t9MpIy0La+DYnxiWGMLIjefZeJDxER8bGXXazP9lKJKuhSdYhz808iDnHITsmGSlSFObIgcpn2roERKnBlUyKiWMPkZ0RlZSWam5tx7NgxuUORhSAIqC6thhXWcd+3worq0moIghDmyILIZdq7Dh2oxyKoMCRTQEREJAcmP+SwdOZS6LV6iILotF0UROi1eiyduVSmyIJk4UIgKwsYlcDdhPdxCrPQiCI0ogiv4D4ZAyQionBg8kMO9uqPxWZx2m6xWSK/6gNI09537hyzWYcOFOFDFOFD5ONvMgRGREThxOSHnLhWf6Km6mNXUQHs2ydVgMbh6zighARpKFFTk/Rizx8iIuVj8kNOXKs/UVP1Ga2iAmhrA956S+pYmJ7ueEuHDuzXfM/rU126JM2gZ9NDIqLIweUtXHB5C8Bms6HkpRIc6z4GvVaP9x96P7qSH1cWi7T6e08PkJmJpqsWYn6xOPFxbjQ2AkVFQYyPiIgm5MvnN/v80BiCIKBmcQ3Wvb4ONYtrojvxAaSxQIsWXfm+SbZIiIgoDJj80LjKrilDc2UzOkwdaOpxnw1EdNdnIiKKSUx+RtTW1qK2thYWi2XinWNETHR9JiKimMMBzyNivcnheGKi67OdxQLU1wO//S00p45CleD/UDiXRtJERKQwTH7IrZjo+gwA+/cDublAaSlw333Q/eNXsf/y7X6frqKCM76IiJSMyQ95FPVdn/fvB1asADo7nTbPsZ2AGhf9OuXwMNdPJSJSMiY/5FFUd322WID164Fxuj3o0IEW5HG5CyKiKMQBzzQhe/WnqacJFpsFoiCiKLMo8qs+R46MqfiMpkMHFuJ/oMZFDGKST6fes8fRNggAoNEAOl0gwRIRUbCw8kMTitquz16MTLZXgBpRhNfwDVTjX7w69b/9Gzs/ExEpFZMf8oq9+gMgOsb6AFfKMhOwL3x6G17HN/CGX5caHOQ4ICIipWDyQ16xd33O1+RHT9fnhQulBU69/VkEAZieEdqYiIgo5Jj8kNfsXZ/LrimTO5TgEEVg507pzxMlQPb3N24MbUxERBRyTH4otlVUAPv2ATNmOG+Pc/nVyMoC9u2D5p5boIqCno5ERLGMyQ9RRQXQ1ga89RawYQOQng5YRzV21GikEcwVFdDppNZAREQUuZj8jKitrUVBQQH0er3coZAcRBHo65MegxkMzu998QVw772OrMfLcdJERKRQgs02Toe3GNbf34/U1FSYTCakpKTIHQ6Fi8UiLXHhoe8PsrKAtjY0fSRi/nzfL9HYCBQV+R0hERF54MvnNys/RMCEDQ8BSO8//TSGhnw/vVotPT0jIiL5scMzEeD9UuybNiFx8j8AuGXCXX/4QyA7W/rztddKfX5ce/2w8zMRUfgx+SECfBvIs307vEl+/u3fJj6VWg20tDABIiIKJyY/5JMOUwcMFwxu35921TRkpWSFMaIgsTc8nOjRFwD0ngnaZe2dn5n8EBGFD5Mf8trQ5SHoX9Sjd6DX7T4ZSRloW9+GxPjEMEYWBPaGh3fdJXckREQUYhzwTF5TiSroUnWIc/PPJg5xyE7JhkqM0C6AFRXA5s1yR0FERCHG5Ie8Zl/d3QrruO9bYY381d5/8hPp8ZcnqVPCEgoREYUGkx/yiX11d1EQnbaLghgdq72PXu/LHdP5sIRCREShweSHfGKv/lhsFqftFpsl8qs+FgtQXw9cvAh4aJClwRdQYzBol/V2lj0REQUHkx/ymWv1JyqqPvv3Sx2eS0uBVauA/n63u+rQjhZch8b/+wFeeSXwS1dUAO3tgZ+HiIi8E/XJz9tvv43Vq1fLHUZUca3+RHzVZ/9+YMUK76a5j9ChA0XJp7BwodSrJxDDw2ObHxIRUehEdfJz/vx5HDp0CBaLZeKdySf26g+AyK76WCzA+vWAP0vcnToFnU5qUtjYiKBUgYiIKPSiOvl55ZVXcP/998sdRlQSBAE1i2uQr8lHzeKayK36eLOmlztPPQXs3w+dTlqwNBhVICIiCr2oaHK4ceNGGF2eGxQVFeHOO+9k1SeEyq4pw3+t+i8YLhjQ1NM07j6K7/gc4Gjj9u8/C2P2cmmWGIB9+4Bjx4DTp4G+PuD8eeDddyc+z8mTXOeLiChcBJvNn3q/8n3zm9+E2WzG4OAg2tra8Mwzz+C73/3uhMf19/cjNTUVJpMJKR5m/JDU8TlnR05kd3yur5cGOfuhHdnIQwsGMSkooXCdLyIi//ny+a3Ix14HDx5ESUkJ2traHNsGBgawdu1aVFVVYd26dRgaGvJ4jtdeew319fX43e9+h/Lycq8SH/JNVHR8tq/p5cdjOyM0QUt8gCvrfBERUWgpLvkxGAwwm81oaGhw2r5mzRosWbIE27Ztw4IFC1BVVSVThGQXFR2fRzc1VHKcREQUNIp87GW1WiGKIlpbW5Gbm4vu7m7MnDkT586dg1qthsFgQE5ODnp7e5GcnBzQtYaGhpyqSP39/cjOzuZjLy/ZbDaUvFSCpp4mp8aHoiCiKLMI7z/0vrKTH7v9+6VZX6MHP2dlSQ0P+/rGnQ3WhCLMR2NQw2hslAZPExGRbyL+sVdcnHNY9fX10Gg0UI9MpUlPT0diYuKY6pA/tm3bhtTUVMcrOzs74HPGkqjp+FxRAbS1AW+9BezZI31tawN+8QvpfdefI1J+LiIiGkORyY+rrq4upKWlOW1LSkpCd3d3wOeuqqqCyWRyvDo6OgI+Z6yJmo7PoggsWgSsXCl9FUUpKdq3D5gxw3nfrCzguefkiJKIiAIUEVPdBUFwVH3shoeHkZCQEPC5ExMTkZio0JlIEcJe/Sn/j3IAzlWfDlMHDBcMAIAz5jM4N3jO6dg0dRqmJ00HoOBp8cuXA6mp0swwQEqMFi0CPhI9HOSfl14C8vKAa68FMjOlbZwCT0QUXBGR/Gi1WphMJqdtZrMZWq02aNeora1FbW0t+wL5yV79OdZ9zFH1Gbo8BP2Leo9T4UdT5LT48cYC7dolDZLOrQj65f7938du4xR4IqLgiojHXqWlpejs7MTw8DAAOB53FRcXB+0alZWVaG5uxrFjx4J2zlgyXsfniabCj6bIafHu1vzq6gJWrIDm2Oth6ejMKfBERMGlyMqPfQKa/WtmZibKy8tx+PBhLFmyBAcOHMDatWvHPAojeY3X8fn+uffjWPfECaXipsV7WvNrZJvuX7+Dlmf+L4yTsoHCQvScFXH+PHDqlNTheXAQGBgAXn898HD+3/+TukCPNmUKMGcOK0JERL5S3FR3s9mM3/zmN1i7di02bdqE73//+9BoNDAajfjxj3+M3Nxc9PX14ZlnnoFKFfwqATs8+8+bjs/jUeS0eB87P7dnFCPvi3cxeCn444A8SUwEPv2UCRARkS+f34qr/CQlJWHNmjVYs2aN03aNRoOXXnopZNflmJ/A2R9zGQYMbhsfjkeR0+J9XPPLeOYyBhHexAcAhoakR2JMfoiIvBcRY37CgWN+AjdRx+fxKHZavH2qldcUVUAlIiIPmPxQULn2/LFzN+hZkVUfIKA1v4iISNmY/Iyora1FQUEB9Hq93KFENHcdn62w4rq06wAACzIXYIF2AQAos+oDcM0vIqIoxuRnBB97BY+7js8vfOMF5Gvysa1sG7Yt3uY0LV6R3HV3JiKiiKa4Ac8U+dx1fF4ycwmaK5sd+43+s2JVVABWK3D33XJHQkREQcLKD4WEvfoDKPjRljcsFuAHP5A7CiIiCiImPxQS43V8jkhHjozt8DwODYxQ46LHfRITpVcwJSZKa38REZH3+NhrBPv8BF/ZNWWR8WjLEy/7/ejQgRbkwYhRmUj1VuAb33B8OzQE9PUB588Dra3Av/6rfyFVVwPJydKfr71W6vPjuvwFF0MlInJPcR2e5cYOz+TEx07PTtLTgbo6oKIC7e3Sau2Dg4GH9Npr0pJjns7FxVCJKNb48vnNx15EngTS78dolLKU/fthNAYn8QGkytFE5+JiqERE7jH5IfLE3u/HnwKp/ZgNG6SB00REpAhMfogmUlEBbN7s37E2G9DRAXz4YVBCSUyUVnMnIiL/ccDzCA54Jo9mzQrseC+fQf3wh1eSm+Rk55lcU6YAc+bwcRYRUaCY/IyorKxEZWWlY8AUkROfFzp14eV89PvuA4qKPO/D5IeIKDBMfkiROkwdMFwwuH1/2lXTkJWSFbTjJmQf+NzV5dv4H0GQjiss9Gr3deukr4OD0iMuQbjy59RUYOpUYGDAu0ufPOn+PU6FJ6JYxuSHFGfo8hD0L+rRO9Drdp+MpAy0rW9DYnxiwMd5xT7wecUKKSPxJgGyzxDbsUM63gvvvONbWJ6sWuX+PU6FJ6JYxgHPpDgqUQVdqg5xbv55xiEO2SnZUImqoBznNV8XOs3KkvavqIBGIyUcSsGp8EQUy1j5IcVxXRjVlRVWVJdWj1kyw9/jfFJRASxfLi170dMDTJsmbT971vnPmZnSo7KRio9OJ1VajEbpcZSnqgwREYUWkx9SJPvCqE09TbDYrszAEwURRZlFbhdK9fc4n4gisGiRz4fpdHzMRESkBHzsNaK2thYFBQXQ6/Vyh0K4UsUZncAAgMVm8Vi98fc4IiKKHUx+RlRWVqK5uRnHjh2TOxQaYa/iiIL06EgUROi1+gmrN/4eR0REsYHJDymWaxXH2+qNv8cREVFs4JgfkoW3/XjsVZxj3cd8qt74e5xfLJYrA6BdBjoTEZHyMPmhsPO1H0/N4hqse30dahbXeF29EQTBr+N8tn8/sH490Nl5ZVtWltQTqKJi3EM0Gqlp4dBQaELyhlrtddNpIqKoI9hs/ixXHb3sy1uYTCakpKTIHU5UstlsKHmpBI3djbDCOub9OMRhvnY+3n/ofWU/qtq/X2p66PorZI95pMfPeNrbgT/+UVrwPZxeeQXIz2eHZyKKPr58fnPMD4WdfUzOeIkPEKR+PKFmsUgVn/H+38G+bcMGab9x6HTS07Fwy8+X1g5j4kNEsYzJD8nCdUaWXcTMzDpyxPlRlyubDejokPYjIiJFYfIzgn1+wivi+/H09AR3PyIiChsmPyPY5yf8IrofT2ZmcPcjIqKwYfJDsonofjwLF0qzutzFKghAdrY8A3uIiMgjJj8kK3v1B0DkVH0AqY/Pzp3Sn10TIPv3O3aw3w8RkQIx+SFZ2fvx5GvyQ9uPJxQqKqTp7DNmOG/PyvI4zd1Oo5H67YQLe/sQEUnY58cF+/yQzwLo8NzeDhiNztt6eoDz56U/T5niPGzIPn7avq2nB/jsM+DLL52PsUtOBq69VtqfvX2IKJr58vnNDs9EgRJFYNEivw7V6ZiQEBGFG5Mfkp19na8z5jM4N3jO6b0vLnwBAQJssDm+9g/1O95PTUxF2uQ0pKnTMD1pumNNsLCYoOLj7fpl7niqChmNwLlzgNkMXLggvffll9LX5GRg8mQpvIEBIH7kt/zyZeDiRaCvDzh7VtqWni4NURoakvafNEl6PJaeLo3Xtp9Po7lShfKmgjRe7KOxCkVEcmLyQ7LyZp0vX4xeEyykJljTy9f1y1y1twN5ecDgYCiCD4xaDbS0uE9evIl9onMQEYUSBzyTrFSiCrpUHQQEPtA5DnHITsmGSlQFITIP7Gt6uXZ47uqStu/f7/i54tz8ik0Uq9GozMQHkOLyVNXxJvaJzkFEFEpMfkhW9l4/NgQ+7j4sa4J5uaaXYLVG/vplRERRiskPyc7e68ddlcQbYesO7cOaXhG/fhkRUZRi8jOCa3vJZ6JV3r0Rtu7QPqzpFfHrlxERRSkmPyO4tpe83FV/REHEgswFWKBd4LYyFNZKio9rekX0+mVERFGKyQ8pgrvqj8VmwdZbtmJr6Va3laGwVlJ8XNMrotcvIyKKUkx+SDFGr/M1OWEygCvrfY33nus+YeHHml4Ru34ZEVGUYvJDijF6na9NN29yWu9rvPdc9wkbH9f08mf9snCv++WLidYI8yZ2rjNGRHLi2l4uuLYXeS2ANb28wQ7PRETe8+Xzm8mPCyY/REREkceXz28+9iIiIqKYwuSHiIiIYgqTHyIiIoopTH6IiIgopjD5ISIiopjC5IeIiIhiSlQnP3/+85+RkZGBnJwcnD9/Xu5wiIiISAHi5Q4glE6ePIkzZ87IHQYREREpSNRWfj7//HPs3bsX+fn5aGpqkjscIiIiUoioqPxs3LgRRpde+k888QQ+/PBDHD16FN/5zndw4sQJrqQdRTpMHTBcMOCM+QzODZ4b836aOg3Tk6Zj2lXTkJWS5dgfwLjH2PcH4HTMX8/+ddzz24+ZM30OslKyrsTV1wbDkTeAY8eA7m5AEHBmehLOJYvAwAWkCZNhu/56nLNdAPpN+OIqEcLMmUiblAa0tAB9fbD0myCmpAJpaUibdxOsA5k4duzKkhRXYRpShSycP2fBhc96MNnSjyHVVbBMz8KXZhF9fVeWqxBFYHAQuHQJSEgA+tGBy4kGCAJgswGXLgOXL0l/tlgAm3ka0J+FgQHp+7g46ZWQAKhU0tIXU6YAs2YBBQVXlr4YHgamTZPiGx0rMHbZjRtuAG68Udp25mIHzpoNMJmu7AdIS3hcuABYbJeRfFW8Y6mNL7+U3gOkOFLip2FqfJbje3fLb4y+/656eoC4i9MwfVLWuO9zKQ6iwP3lb3/BibMn3L4/L2MevnndN8MWT0wsb1FZWYnq6mqkpaVNuC+Xt1C+octDyNmRg96B3gn3zUjKQEtlC6574Tqv9h99zKyfz8LZC2c97jv9qun4fMPnSIxPxNC+vchpWIneq0L8K/VlBrCjDbAk+nacOAT8IAdI8vD34O+5/eFNPNY4IM7q/v1x4lWrpTzSnrB49e/Fw8/tej4i8o3BbMC0f5s24X5nf3gW6Unpfl8n4pe3OHjwIEpKStDW1ubYNjAwgLVr16Kqqgrr1q3D0NCQx3OMzumys7O9SnwoMqhEFXSpOgjwXMmLQxyyU7KRpEqCLlWHOC/+ubseM5Hs1GyoRBWwfz9Ud/8jdOdtHj+rA2aNA/qzAYvK92MtKsCkk84R7HP7Y8J4BOCy2ud4BwedF1W1/3txe/8n+Lldz0dEvkmblDbhf68FCFIFPEwUl/wYDAaYzWY0NDQ4bV+zZg2WLFmCbdu2YcGCBaiqqvJ4nt27d2PZsmXYsWMH7rrrrlCGTGEmCAKqS6thg+cKixVWVJdWIy4uDtWl1bBi4qxk9DFbb9k64f5bS7dCsFqBdesgAKh+0/1ndVDEWYE3q4EJ/kMyPkE61l12FtC5/TFRPDbg7X8JOF77vxe39z/sPzdRbBFFEfffeL/Hfe6/8X6IohimiBT62MtqtUIURbS2tiI3Nxfd3d2YOXMmzp07B7VaDYPBgJycHPT29iI5OTmgaw0NDTlVkfr7+5Gdnc3HXgpns9lQ8lIJGrsbx/1QEwURRZlFeP+h9yEIgmP/pp4mWGyWcc853jHFLxbjg54Pxt1/gXYBGh5qgHD4MFBaKsUFoORhoCkTsIxOguy/ZYLLNneft+PtbxWBniLgxfc9HDgRG/BwCZDZBMSN+nsIyrlDEc97wMP/y+d4GxuBoqJRV3Fz/+Mgwto18c/tej4i8o3FYkHC1oRx/6dVgIBL//tSwMlPxD/2iotzDqu+vh4ajQZqtRoAkJ6ejsTExDHVIX9s27YNqampjle2fWQlKdpE/zdvsVlQXVrtGORu399d4uPuGE/Vn62lW6V9e3quxAWp+mNx/c0SMPaz1VOOMd7+cZYgVCjs1RaXv4egnDsU8cQFJV53998KuX5uotjiqfoT7qoPoNDkx1VXV9eYMTtJSUno7u4O+NxVVVUwmUyOV0dHR8DnpPBYOnMp9Fr9mLEcoiBCr9Vj6cyl4+4vCmN/yTwdsyBzwZj9F2gXXNk3M9P5mNOAvgsQR/Iy0Qos6JJe9m1xVmDy8JXvYYOj2jN6f8cTH6sIdOmB087x+eX0UulcVjH45w5FPEGK1/X+i4KIglQZf26iGPPLO345ZuyPAAG/vOOXYY8lIpIfQRAcVR+74eFhJCQkBHzuxMREpKSkOL0oMrir/rhWcFz3H6/64+mY8ao/jqoPACxcCMyYceUYOFd/LHHA1jell32bNQ74l7dHVYhGVXpG7+8YPxTUyoxLtUW2qo+38QQnXtf7b7FZsGY2qz5E4TJe9UeOqg8QIcmPVquFyWRy2mY2m6HVaoN2jdraWhQUFECv1wftnBR69v+bB4DJCZMBYNwKznj7L8hcgAXaBV4dM7r641T1AaRmOj/7mfMxI9UfQPq69PTYbVX/c+V7e6XHdf+CrknSxmBXZuzVlFCcOxTxBCne0fdfr9XjpnRWfYjCaXT1R66qDxAhyU9paSk6OzsxPDwMAI7HXcXFxUG7RmVlJZqbm3Hs2LGgnZNCTxAE1CyuQb4mH5tu3oR8TT5qFte4bWg5ev9tZduwbfE2r47ZVrYNuhQddKk6bFu8bey+FRXAq68CU6dKxwCoOQTkG6Svwjjb4uzf98Vh27FkbBtn/598mAMY8oFDNQhuhUKQzhmSc4ciHu/jVaulxoTjXmXU/a9ZXIP0dAEuRWWfzkdEvhFFEasLVwMAVheulqXqAyh0tpfNZkNcXBz+/ve/4+qrrwYArFixAt/73vewZMkS7Nq1C5988gmee+65oF+bTQ4pIBYLUF8PvPkm0NYGCAKQnQ2kpUmtiePipMdkoii1Qc7MlL4HgCNHgK4uwGAA0tOlR2kLF6K9S8ShQ9Jbo/nT4XlwUDo2Lg6wWqXtw8NSh+fLl6+8wtXhGZBi7eu70rkZkPrqjD7u2mtHfubzV3ruuCYknjo8e9Le7rmPDzs8E0UGXz6/FZf8mM1m/OY3v8HatWuxadMmfP/734dGo4HRaMSPf/xj5Obmoq+vD8888wxUquA1Y6utrUVtbS0sFgs+/fRTJj9EREQRJKKTH7mx8kNERBR5Ir7PDxEREVGoMPkhIiKimMLkZwSnuhMREcUGjvlxwTE/REREkYdjfoiIiIjcYPJDREREMYXJDxEREcWUeLkDUAp7k8PLly8DkJ4dEhERUWSwf257M5SZA55ddHZ2Ijs7W+4wiIiIyA8dHR3IysryuA+THxdWqxXd3d1ITk52WrxSr9cHZdFTX8/j7f4T7efpfXfvjbd99Lb+/n5kZ2ejo6Mj7DPjeD/GbuP98H8/3o/gnIf3Y3y8H2O3heJ+2Gw2fPnll9BqtYiL8zyqh4+9XMTFxY2bMYqiGJQb5Ot5vN1/ov08ve/uvfG2j7ctJSUl7P8x4f1wv433w/f9eD+Ccx7ej/HxfrjfFuz7kZqa6tV+HPDspcrKSlnO4+3+E+3n6X137423PVh/D4Hi/fAtnlDj/fAtnlDj/fAtnlDj/fAtnnDgYy/yGxtCKgvvh7LwfigL74eyyH0/WPkhvyUmJmLTpk1ITEyUOxQC74fS8H4oC++Hssh9P1j5ISIiopjCyg8RERHFFCY/REREFFOY/BAREVFMYfJDREREMYXJD4XEO++8g7KyMrnDIAAGgwF33HEHcnJy8Mwzz8gdTswbGBjAQw89hBtuuAF79+6VOxwa8fbbb2P16tVyh0EA/vznPyMjIwM5OTk4f/58SK7B5IdC4mtf+xouXLggdxgEoKmpCfv27UNjYyO2b9+Ovr4+uUOKab29vairq8OBAweY/CjE+fPncejQIVgsFrlDIQAnT57EmTNn8Pnnn2PKlCkhuQaTHwoZlUoldwgEYNmyZVCpVNBoNCgoKGCDN5ldc801iI+PR2dnJ9atWyd3OATglVdewf333y93GATg888/x969e5Gfn4+mpqaQXYdrexHFiI6ODixbtgzx8fy1l1t7ezuqqqqQlpaGRYsWyR1OTPvLX/6C5cuXs+qjEDk5Ofjwww9x9OhRfOc738GJEyecFhkPFv5XkCgG2Gw2/P73v8ePfvQjuUMhADqdDgcPHsS8efNgMBiQnp4ud0gx6xe/+AXMZjMGBwfR1taGXbt24bvf/a7cYcW8m266CV//+tdx7tw5pKWlBf38fOxFEzp48CBKSkrQ1tbm2DYwMIC1a9eiqqoK69atw9DQkHwBxhh/7seePXuwevVqxMfHo7e3N8wRRzd/fz8EQUBxcXFI/sMey3y9H6+99hrq6+vxu9/9DuXl5Ux8gszX+zF60Yns7OyQ/X4w+SGPDAYDzGYzGhoanLavWbMGS5YswbZt27BgwQJUVVU5vf/xxx/j9OnT+OSTT8IZbtTz535UV1fjqaeeQllZGWbPno2WlpZwhx21/Lkfu3btwgMPPIDdu3fjkUcegSiK4Q47avn73ysKDX/ux+7du7Fs2TLs2LEDd911V8hi49peNCGr1QpRFNHa2orc3Fx0d3dj5syZOHfuHNRqNQwGA3JyctDb24vk5GS5w416vB/KwvuhLLwfyqLU+8HKD00oLs75n0l9fT00Gg3UajUAID09HYmJiWOyewoN3g9l4f1QFt4PZVHq/WDyQz7r6uoa8xw2KSkJ3d3dMkUU23g/lIX3Q1l4P5RFKfeDyQ/5TBAER9ZuNzw8jISEBJkiim28H8rC+6EsvB/KopT7weSHfKbVamEymZy2mc1maLVamSKKbbwfysL7oSy8H8qilPvB5Id8Vlpais7OTgwPDwOAo1xZXFwsZ1gxi/dDWXg/lIX3Q1mUcj+Y/NCE7BMC7V8zMzNRXl6Ow4cPAwAOHDiAtWvXjillUmjwfigL74ey8H4oi1LvB5Mf8shsNqOurg6A1H/BaDQCAOrq6rB3715s3boVJ06cwNNPPy1nmDGD90NZeD+UhfdDWZR8P9jnh4iIiGIKKz9EREQUU5j8EBERUUxh8kNEREQxhckPERERxRQmP0RERBRTmPwQERFRTGHyQ0RERDGFyQ8RERHFFCY/REREFFOY/BBRTNi4cSO+9a1vyR0GESkAkx8iignHjx/HvHnz5A6DiBSAyQ8RxYSPPvqIyQ8RAWDyQ0QxoLOzE0aj0ZH8nD9/Hrfffjv+4R/+AWfOnJE3OCIKOyY/RBT1jh8/jilTpiA3Nxd//etfodfrMWPGDLz11lvIyMiQOzwiCjMmP0QU9Y4fP465c+diz549uPnmm/HEE0+grq4OCQkJcodGRDIQbDabTe4giIhCacWKFXjzzTcBAP/5n/+Jm266SeaIiEhOrPwQUdQ7fvw4KioqMDg4iPPnz8sdDhHJjJUfIopqX375JVJTU9HY2IgPP/wQP/jBD/Duu+/i+uuvlzs0IpJJvNwBEBGF0kcffQRRFFFQUIDCwkJ8/PHHuP3229HQ0ACNRiN3eEQkAz72IqKodvz4ccyePRuJiYkAgOeeew55eXmoqKjA8PCwzNERkRz42IuIiIhiCis/REREFFOY/BAREVFMYfJDREREMYXJDxEREcUUJj9EREQUU5j8EBERUUxh8kNEREQxhckPERERxRQmP0RERBRTmPwQERFRTGHyQ0RERDGFyQ8RERHFlP8P1faUy9ZYYpIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 100000\n",
    "m = 3\n",
    "alpha1 = 0.5\n",
    "alpha2 = 1.0\n",
    "alpha3 = 1.5\n",
    "\n",
    "G1 = ig.Graph.Barabasi(n, m, directed=False, power=alpha1)\n",
    "G2 = ig.Graph.Barabasi(n, m, directed=False, power=alpha2)\n",
    "G3 = ig.Graph.Barabasi(n, m, directed=False, power=alpha3)\n",
    "\n",
    "\n",
    "k1, pk1 = get_pdf_igraph(G1)\n",
    "k2, pk2 = get_pdf_igraph(G2)\n",
    "k3, pk3 = get_pdf_igraph(G3)\n",
    "plt.plot(k1, pk1, \"ro\", label=r'$\\alpha = 0.5$')\n",
    "plt.plot(k2, pk2, \"bs\", label=r'$\\alpha = 1.0$')\n",
    "plt.plot(k3, pk3, \"gv\", label=r'$\\alpha = 1.5$')\n",
    "plt.legend(loc=0)\n",
    "plt.xscale(\"log\")\n",
    "plt.yscale(\"log\")\n",
    "plt.xlabel(\"$k$\")\n",
    "plt.ylabel(\"$p(k)$\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65e5e66c",
   "metadata": {},
   "source": [
    "## 比安科尼-巴拉巴西模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4e4b7ea4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def bianconi_barabasi_model(n0, n, m, etas, i0, i1, i2):\n",
    "    # 初始化网络\n",
    "    G = nx.complete_graph(n0)\n",
    "    degrees = np.zeros(n)\n",
    "    for i in range(n0):\n",
    "        degrees[i] = n0-1\n",
    "    \n",
    "    # 记录节点i0, i1, i2三个节点的度值\n",
    "    deg_i0 = []\n",
    "    deg_i1 = []\n",
    "    deg_i2 = []\n",
    "        \n",
    "    # 生成网络\n",
    "    for j in range(n0, n):\n",
    "        if i0 < len(G.nodes()):\n",
    "            deg_i0.append(G.degree(i0))\n",
    "        if i1 < len(G.nodes()):\n",
    "            deg_i1.append(G.degree(i1))\n",
    "        if i2 < len(G.nodes()):\n",
    "            deg_i2.append(G.degree(i2))\n",
    "        \n",
    "        \n",
    "        # 计算节点的连接概率\n",
    "        probabilities = etas[:j] * degrees[:j]\n",
    "        probabilities /= np.sum(probabilities)\n",
    "        \n",
    "        # 选择节点连接\n",
    "        targets = np.random.choice(j, size=m, replace=False, p=probabilities)\n",
    "        \n",
    "        # 更新网络和节点度\n",
    "        for j0 in range(m):\n",
    "            G.add_edge(j, targets[j0])\n",
    "            degrees[targets[j0]] += 1\n",
    "            \n",
    "        degrees[j] = m\n",
    "    \n",
    "    return G, deg_i0, deg_i1, deg_i2\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d7f1725c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '$k(t)$')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n0 = 3\n",
    "n = 10000\n",
    "m = 2\n",
    "etas = np.random.uniform(0, 1, n)\n",
    "\n",
    "# 设置指定节点的适应度值\n",
    "i0, i1, i2 = 1, 10, 100\n",
    "etas[i0] = 0.2\n",
    "etas[i1] = 0.5\n",
    "etas[i2] = 0.99\n",
    "\n",
    "\n",
    "samples = 100\n",
    "y0 = np.zeros(9997)\n",
    "y1 = np.zeros(9989)\n",
    "y2 = np.zeros(9899)\n",
    "for i in range(samples):\n",
    "    G, deg_i0, deg_i1, deg_i2 = bianconi_barabasi_model(n0, n, m, etas, i0, i1, i2)\n",
    "    y0 += np.array(deg_i0)\n",
    "    y1 += np.array(deg_i1)\n",
    "    y2 += np.array(deg_i2)\n",
    "\n",
    "# 绘制指定节点的度与时间的依赖关系\n",
    "plt.plot(range(i0, i0+len(y0)), y0 / samples, 'r-', label=r'$\\eta = $'+str(etas[i0]))\n",
    "plt.plot(range(i1, i1+len(y1)), y1 / samples, 'b-', label=r'$\\eta = $' + str(etas[i1]))\n",
    "plt.plot(range(i2, i2+len(y2)), y2 / samples, 'g-', label=r'$\\eta = $' + str(etas[i2]))\n",
    "\n",
    "\n",
    "plt.legend(loc=0)\n",
    "plt.xscale(\"log\")\n",
    "plt.yscale(\"log\")\n",
    "plt.xlabel('$t$')\n",
    "plt.ylabel('$k(t)$')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
